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We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-dependent effective dimension that reflects the geometry of the gradient model at the trained parameters, through the empirical Jacobian Gram matrix and a residual-curvature term. In the linear case, where the curvature term vanishes, this recovers the classical effective dimension of the Jacobian kernel covariance, but evaluated at the trained model rather than at initialization as is typical in neural tangent kernel analyses. We further bound this effective dimension via covering complexity of the gradient features, leading to guarantees that depend on learned geometry rather than parameter count. In particular, for manifold-supported data and piecewise Lipschitz Jacobians, the bounds scale with intrinsic dimension, while for one-hidden-layer ReLU networks, the mechanism can be made explicit through counts of activation-stable regions. Experiments on synthetic manifolds, clustered distributions, and benchmark datasets illustrate trained-Jacobian compression, the tightness of the residual-curvature linearization, and agreement between the stability bound and observed generalization gaps. A key feature of our bounds is the simplicity of their derivation, which follows from first principles using the Brascamp-Lieb inequality under strongly log-concave noise.

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Maria Koskinopoulou is an Assistant Professor in Robotics and Computer Vision at Heriot-Watt University. Her research interests include robotic manipulation, perception, robot vision, medical robotics, human-robot interaction, and machine learning. She is involved in major UKRI and EU-funded research projects advancing robotic manipulation, surgical and underwater robotics, autonomous assembly, and waste sorting. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Artificial intelligence systems that ask users to pause to consider AI's energy consumption and environmental impacts are likely to reduce unnecessary AI use, new research by Oregon State University suggests. The findings, published in Science Communication, are important as AI is already using electricity on scales that can be meaningfully compared to households, factories and towns. For example, the electricity needed to train a large language model would power 120 homes for a year, the researchers note; one AI-generated image has roughly the same energy cost as charging a smartphone. With about 85% of the world's energy still coming from fossil fuels, every megawatt-hour that can be carved from AI's electricity profile is significant, says the study's leader, Cheng "Chris" Chen of the OSU College of Liberal Arts. "Despite AI's substantial environmental impacts, information about those impacts is rarely disclosed or effectively communicated to everyday users of AI systems," said Chen, assistant professor in the School of Communication.

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'Record the faces': Tense moment NBA boss gives VERY honest take on Trump attending Knicks game Leaked transcript of UNAIRED 60 Minutes interview exposes REAL reason'callous' CBS star Scott Pelley'deserved to be fired' Disgraceful texts'hot' teacher sent boy, 17, who she had illegal sex with where she moaned about her HUSBAND Everyone always said I cleared my throat a lot. But then I developed shoulder pain and doctors discovered the sinister cause... the world's deadliest cancer. Don't leave it too late like I did Outrage as Netanyahu is caught SPYING on Trump's Iran negotiators... as JD Vance reveals a chilling truth about Israel White couple gave birth to'non-Caucasian' baby. Parents were told son, 7, had ADHD... not realizing he was battling terrifying disease that has now left him BLIND'Great' mom, 32, tried to gas herself and her three young kids to death after inviting them to'popcorn sleepover' in car, prosecutors allege Medical student, 24, died by suicide in his white coat a day after he was suspended for alleged'inappropriate' behavior towards female patient, lawsuit alleges, as his heartbreaking goodbye note to parents is revealed Karmelo Anthony's parents seen leaving the courtroom in tears just before son's defense team pulls shock move Grim-faced former Louisiana mayor Misty Roberts arrives in court for sentencing after being found guilty of having sex with son's teenage friend Mother died during tummy tuck and Brazilian butt lift after clinic staff failed to hold'slow' elevator for EMTs, report alleges Gaming influencer Alex Cimo dies'very suddenly' aged 32 just a month after'refusing to accept his fate' The porn-fuelled fantasy middle-class husbands are desperate to try with their wives... and it almost always ends in divorce: JANA HOCKING All the backstage gossip from Miami Swim Week: Insider exposes'catty' VIP's diva demands... STEALING... and'morbidly embarrassing' celeb moment everyone is whispering about Girl, 13, mistakenly told she was DYING after Oregon hospital staff made jaw-dropping surgical mistake, parents' $17m lawsuit alleges Mother's final words before she was shot dead'by new husband' in front of her two young children'They have a problem with my country': Africa's best referee, who was denied entry to the US and will miss the World Cup, speaks out and insists he had a valid visa Furious dad films his partner in bed with his 19-year-old son: You've seen the viral video - now all three tell the Daily Mail what REALLY happened in the scandal gripping Australia It's official - Britain is crazy for salmon! New figures released by Salmon Scotland have revealed how demand for the humble fish has soared over the last year.

The next decade won't be Armageddon. But it will bring a lot of change. When I started this six-week-long series by asking whether my daughter would be attending college in 2035, the year she turns eighteen, I was pretty sure I already knew the answer. Nine years is a short amount of time, and something disastrous--or, I suppose, downright liberatory--would need to happen in the culture to make it unlikely for her to shuttle off to some campus after high school. Still, thinking about the future of higher education has convinced me that her path to a bachelor's degree will be very different from the one I began in 1998.

The number of public school students requiring special Japanese-language instruction reached a record high of 84,759 in fiscal 2025. The education ministry plans to launch a model project in fiscal 2027 to provide basic Japanese-language instruction for school life and classes to children of foreign nationals living in Japan. In response to an increase in the number of such children, the ministry aims to establish guidelines for effective language lessons through the project. The number of public school students requiring special Japanese-language instruction, including those who are unable to communicate adequately in daily Japanese conversation, reached a record high of 84,759 in fiscal 2025, which ended in March this year. The number doubled over the past nine years, according to the ministry. Of those students, about 10% were not given sufficient instruction at their schools due to staff shortages and other reasons.

Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strategies that generalize across tasks. Modular skills can provide such reusable strategies, yet existing skill-augmented RL methods decouple skill creation from policy optimization, risking adopting skills that conflict with the evolving policy. Inspired by Anthropic's Skill Creator, we introduce RESKILL, an RL-in-the-loop skill creation framework that reconciles skill evolution with policy learning. RESKILL exploits the group-wise structure of GRPO to naturally embed three mechanisms with only marginal additional overhead: (1) an assertion-driven skill creator that diagnoses failures from past experience and proposes conditional, trigger-based skill revisions; (2) within-group rollout sampling that enables controlled comparison of skill versions, capturing which version best supports the policy's ongoing learning; and (3) Thompson Sampling with adaptive discounting to balance exploration and exploitation in skill version selection as the policy evolves. Across several domains, RESKILL consistently outperforms existing memory and skill-based RL methods, with the largest gains on unseen tasks. Analysis of the skill lifecycle shows skills being automatically created, tested, refined, and pruned as the policy improves, demonstrating reconciled skill-policy co-evolution.